{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "arctic-genius",
   "metadata": {},
   "source": [
    "### 下载安装包Anaconda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affiliated-intermediate",
   "metadata": {},
   "source": [
    "通过清华大学镜像站下载\n",
    "\n",
    "https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recreational-workplace",
   "metadata": {},
   "source": [
    "### 测试环境是否安装成功？"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proud-reason",
   "metadata": {},
   "source": [
    "通过运行python 和 conda 来验证是否安装成功"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "norwegian-jacksonville",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_10-30-50.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "designing-spanish",
   "metadata": {},
   "source": [
    "* conda  -V   查看版本号\n",
    "* conda  -h\t\t\t\t\t\t    查看帮助文档\n",
    "* conda  env  list\t\t\t\t\t    查看虚拟环境列表\n",
    "* conda  create  -n  your_env_name\t \t    创建虚拟环境\n",
    "* conda  create  -n  your_env_name  python=3.6\t    创建虚拟环境并指定python版本\n",
    "* activate  your_env_name\t\t\t\t    进入某个虚拟环境\n",
    "* conda deactivate \t\t\t\t\t\t    退出虚拟环境\n",
    "* conda  install  package_name\t\t\t    安装指定的包\n",
    "* conda list -n your_env_name            列出指定环境下的包\n",
    "* conda  uninstall  package_name\t\t\t    删除指定的包\n",
    "* conda  remove  -n  your_env_name  --all\t    删除某个环境\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "editorial-berkeley",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_10-36-50.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mathematical-antenna",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_10-38-54.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loving-adolescent",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_11-03-28.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-range",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_11-04-19.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "integral-chosen",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_11-04-56.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "african-firewall",
   "metadata": {},
   "source": [
    "### 安装jupyter notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "partial-uruguay",
   "metadata": {},
   "source": [
    "![](imgs/note.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorrect-draft",
   "metadata": {},
   "source": [
    "![](imgs/Snipaste_2021-09-10_11-17-40.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "activated-schedule",
   "metadata": {},
   "source": [
    "### 添加代码提示"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "different-problem",
   "metadata": {},
   "source": [
    "conda install -c conda-forge jupyter_contrib_nbextensions\n",
    "\n",
    "jupyter contrib nbextension install --user\n",
    "\n",
    "conda install -c conda-forge jupyter_nbextensions_configurator\n",
    "\n",
    "jupyter nbextensions_configurator enable --user"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "third-negotiation",
   "metadata": {},
   "source": [
    "### jupyter notebook快捷键"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "generic-albany",
   "metadata": {},
   "source": [
    "* shift+enter 运行本单元，选中下一个单元\n",
    "* ctrl+enter 运行本单元代码\n",
    "* alt+enter 运行本单元代码，并在下方插入一个单元\n",
    "\n",
    "以下快捷键是在命令模式下使用，注意命令模式是不能编辑内容的，光标也不会闪烁\n",
    "* esc 从编辑模式退出到命令模式\n",
    "* enter 从命令模式进入到编辑模式\n",
    "* Y 切换回代码模式\n",
    "* M 切换到markdown模式\n",
    "* A 在上方插入单元\n",
    "* B 在下方插入单元\n",
    "* dd 删除当前行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "abstract-brazil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "a   # shift+enter 运行当前代码段，并移动到下面一行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "christian-lindsay",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 2  # ctrl + enter 运行当前代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "pretty-circular",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a   # alt + enter 运行当前代码段，并在下方插入一行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modular-klein",
   "metadata": {},
   "source": [
    "**jupyter notebook的运行顺序是 按照编号来的**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "spatial-delay",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fewer-gospel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "junior-weekly",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "multiple-present",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [1, 2, 3, 4, 5]\n",
    "y = [2.3, 3.4, 1.2, 6.6, 7.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "damaged-neighborhood",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9f4dee0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "wireless-girlfriend",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "infectious-fancy",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "third-ottawa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
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